基于改进的HMM方法预测驾驶员行为

图6. 9个测试数据集的平均ACC、DR和FAR由不同模型实现 

为了验证模型在驾驶行为预测方面的有效性，使用其他算法进行比较。人工神经网络（ANN）和支持向量机（SVM）等典型算法用于建立驾驶行为模型。在[17]中，作者建立了三个模型，包括ANN，SVM，组合ANN和SVM（ANN-SVM）来估计公路车道下降时的车道变换行为。这两种算法的优点是它们不需要数据处理。为了评估这些方法，将实际驾驶行为与所有数据集的估计驾驶行为进行比较。然后，计算每个驾驶行为的ACC，DR和FAR。每组的相应速率如图6所示。从结果（图6）可以说，在预滤波器的最佳选择之后，所有ACC，DR以及（1-FAR）值都较大超过80％。尽管仍然可以找到一些例外，例如ANN-SVM（保守）的一些ACC高于最佳HMM，但是DR的值减小。为了进一步评估驾驶行为预测的性能，接收器操作特性（ROC）图如图7所示。从结果可以看出，使用最优HMM，DR最高，FAR最低。方法。因此，最佳HMM在所有模型中都具有最佳性能。

图7.不同模型的ROC图

IV.总结和结论 

在该研究中，基于隐马尔可夫模型( HMM )开发了一个驾驶行为预测模型。包括左/右车道变换和车道保持在内的三种不同的驾驶动作被建模为HMM的隐藏状态，并使用驾驶模拟器在高速公路场景中进行模拟。基于HMM，可以通过观察状态推断出不可观察的状态。所考虑的方法基于这样的假设，即相关的物理变量被离散成若干段，以考虑典型的传感器特性。通过寻找最佳预滤波器，而不是优化HMM模型，考虑并改进了HMM的预测性能。在该方法中，基于从9个不同测试驱动程序获得的数据，验证了该方法。每次都选择不同位置的子集进行训练和测试。在相同的实验数据集上，使用观察段范围的一般(预先设置的)值和最终(优化的)值来比较HMM模型。 

最终获得的结果显示HMM识别驾驶员行为的能力显著提高。结果表明，除了分类器（这里：HMM ）之外，组合的预设和适应策略对该方法的统计特性有显著影响。使用最佳参数的HMM模型提高了检测率和准确性，同时降低了误报率。通过选择最佳预过滤参数，预测性能可以得到改善，这一点已在该研究中得到成功证明。 

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作者情况：

1 Qi Deng, Jiao Wang, and Dirk So¨ffker are with Chair of Dynamics and Control, University of Duisburg-Essen, Duisburg, Germany qi.deng, jiao.wang, soeffker@uni-due.de

本文来源于IEEE IV 2018论文集，智车科技（IV_Technology）Darkiller编译，转载请注明来源。